Risk-prediction model has good prediction for in-hospital mortality for pediatric heart transplant
MONDAY, Feb. 13 (HealthDay News) -- A risk-prediction tool using four factors at the time of transplant can help predict post-transplant mortality for children undergoing heart transplantation, according to a study published online Feb. 2 in the American Journal of Transplantation.
Christopher S. Almond, M.D., M.P.H., of the Children's Hospital Boston, and associates studied 2,707 children who underwent primary heart transplants in the United States between 1999 and 2008 to develop and validate a risk-prediction model for post-transplant in-hospital mortality. The model was independently validated in a cohort of 338 children who received transplants between 2009 and 2010.
The researchers found that the best predictive model had four categories of variables: type of hemodynamic support, cardiac diagnosis, degree of renal dysfunction, and total bilirubin. The C-statistic (0.78) and Hosmer-Lemeshow goodness-of-fit (P = 0.89) seen in the development cohort were replicated in both the internal and independent validation cohorts (C-statistic, 0.75 and 0.81, respectively; Hosmer-Lemeshow goodness-of-fit P value, 0.49 and 0.53, respectively).
"We conclude that this risk-prediction model using four factors at the time of transplant has good prediction characteristics for post-transplant in-hospital mortality in children and may be useful to guide decision-making around patient listing for transplant and timing of mechanical support," the authors write.
One of the study authors disclosed a consulting relationship with Berlin Heart Inc.
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